Synthesizing high-quality images from low-field MRI holds significant potential. Low-field MRI is cheaper, more accessible, and safer, but suffers from low resolution and poor signal-to-noise ratio. This synthesis process can reduce reliance on costly acquisitions and expand data availability. However, synthesizing high-field MRI still suffers from a clinical fidelity gap. There is a need to preserve anatomical fidelity, enhance fine-grained structural details, and bridge domain gaps in image contrast. To address these issues, we propose a \emph{cyclic self-supervised diffusion (CSS-Diff)} framework for high-field MRI synthesis from real low-field MRI data. Our core idea is to reformulate diffusion-based synthesis under a cycle-consistent constraint. It enforces anatomical preservation throughout the generative process rather than just relying on paired pixel-level supervision. The CSS-Diff framework further incorporates two novel processes. The slice-wise gap perception network aligns inter-slice inconsistencies via contrastive learning. The local structure correction network enhances local feature restoration through self-reconstruction of masked and perturbed patches. Extensive experiments on cross-field synthesis tasks demonstrate the effectiveness of our method, achieving state-of-the-art performance (e.g., 31.80 $\pm$ 2.70 dB in PSNR, 0.943 $\pm$ 0.102 in SSIM, and 0.0864 $\pm$ 0.0689 in LPIPS). Beyond pixel-wise fidelity, our method also preserves fine-grained anatomical structures compared with the original low-field MRI (e.g., left cerebral white matter error drops from 12.1$\%$ to 2.1$\%$, cortex from 4.2$\%$ to 3.7$\%$). To conclude, our CSS-Diff can synthesize images that are both quantitatively reliable and anatomically consistent.

翻译：从低场磁共振成像（MRI）合成高质量图像具有巨大潜力。低场MRI成本更低、更易获取且更安全，但其分辨率低且信噪比差。该合成过程可以减少对昂贵采集的依赖并扩大数据可用性。然而，高场MRI合成仍面临临床保真度差距的挑战。需要保持解剖保真度、增强细粒度结构细节并弥合图像对比度的域间差异。为解决这些问题，我们提出了一种用于从真实低场MRI数据合成高场MRI的**循环自监督扩散（CSS-Diff）**框架。我们的核心思想是在循环一致性约束下重构基于扩散的合成过程。该方法在整个生成过程中强制保持解剖结构一致性，而非仅依赖成对的像素级监督。CSS-Diff框架进一步整合了两个新颖的过程：切片间隙感知网络通过对比学习对齐切片间不一致性；局部结构校正网络通过对掩码及扰动图像块的自重建来增强局部特征恢复能力。在跨场合成任务上的大量实验证明了我们方法的有效性，实现了最先进的性能（例如PSNR为31.80 $\pm$ 2.70 dB，SSIM为0.943 $\pm$ 0.102，LPIPS为0.0864 $\pm$ 0.0689）。除像素级保真度外，与原始低场MRI相比，我们的方法还能保留细粒度解剖结构（例如左侧大脑白质误差从12.1$\%$降至2.1$\%$，皮质误差从4.2$\%$降至3.7$\%$）。总之，我们的CSS-Diff能够合成在定量上可靠且在解剖结构上一致的图像。